Virtual
Python for Data Analytics
Learn Python, Pandas, NumPy, data cleaning, visualisation and business analytics through a practical online training programme.
A hands-on programme to help participants work with data, analyse trends, build visualisations and present insights for business decisions.
The Programme
Programme Overview
Introduction
Course Overview
Data analytics has become an important skill for professionals who work with reports, business data, dashboards, operational information and decision-making inputs. Python provides a practical way to import, clean, transform, analyse and visualise data using tools such as Jupyter Notebook, Pandas, NumPy, Matplotlib and Seaborn.
This programme introduces participants to Python foundations for data analytics and then moves into practical data handling, cleaning, analysis and visualisation. Participants learn to work with Excel, CSV and other common data sources, perform summary analysis, identify trends and outliers, and develop a practical dashboard or analytical report for business decision-making.
Outcomes
What You Will Learn
- Understand Python and its applications in data analytics.
- Work with variables, data types and operators.
- Use lists, dictionaries, conditions, loops and functions.
- Set up and work with Jupyter Notebook.
- Use NumPy arrays and Pandas DataFrames.
- Import data from Excel, CSV and other common sources.
- Select, filter, sort, transform and combine datasets.
- Clean missing values, duplicates and inconsistent data.
- Create charts using Matplotlib and Seaborn.
- Develop a dashboard or analytical report with insights and recommendations.
Methodology
How We Teach
- Practical explanation of Python foundations for data analytics.
- Hands-on work using Jupyter Notebook.
- Exercises on importing, selecting, filtering, sorting and transforming data.
- Practice with Pandas, NumPy, merge, join and concatenate.
- Data cleaning exercises for missing values, duplicates and inconsistent data.
- Visualisation practice using Matplotlib and Seaborn.
- Practical business analytics project focused on insights and recommendations.
The Outcome
Impact & Audience
For the Organization
Organizational Impact
- Improve data handling and analysis capability within business teams.
- Reduce manual effort in cleaning, transforming and summarising data.
- Enable better analysis of patterns, trends, relationships and outliers.
- Support more structured business reporting through dashboards and analytical reports.
- Improve data-backed decision-making through clearer insights and recommendations.
- Build practical capability to work with Excel, CSV and other common data sources using Python.
For the Individual
Personal Impact
- Build confidence in using Python for data analytics tasks.
- Learn how to work with Pandas DataFrames and NumPy arrays.
- Improve your ability to clean, transform and combine datasets.
- Develop practical skills in grouping, aggregation and summary analysis.
- Learn how to visualise data using charts.
- Gain experience in presenting business insights and recommendations.
Audience
Who Should Attend
- Business Professionals: To analyse data and present insights for decision-making.
- MIS and Reporting Teams: To clean, transform and summarise data more efficiently.
- Excel Users: To move from spreadsheet-based work toward Python-based analytics.
- Analysts and Data Enthusiasts: To build practical Python, Pandas, NumPy and visualisation skills.
- Managers and Functional Teams: To understand patterns, trends, relationships and outliers in business data.
- Students and Early-Career Professionals: To build foundational skills in Python for data analytics.
Curriculum
Course Outline
Session 1: Python Foundations for Data Analytics
- Understand Python and its applications in data analytics.
- Work with variables used in Python programming.
- Understand data types and operators.
- Use lists and dictionaries for handling data.
- Apply conditions, loops and functions.
- Set up and work with Jupyter Notebook.
Session 2: Data Handling with Pandas and NumPy
- Understand NumPy arrays for data handling.
- Work with Pandas DataFrames.
- Import data from Excel files.
- Import data from CSV and other common sources.
- Select, filter, sort and transform data.
- Combine datasets using merge, join and concatenate.
Session 3: Data Cleaning, Analysis and Visualisation
- Handle missing values in datasets.
- Identify and remove duplicate data.
- Clean inconsistent data.
- Perform grouping and aggregation.
- Create calculated columns and apply business rules.
- Build charts using Matplotlib and Seaborn.
Session 4: Practical Business Analytics Project
- Conduct exploratory data analysis.
- Identify patterns in business data.
- Identify trends, relationships and outliers.
- Develop a practical dashboard.
- Create an analytical report based on the data.
- Present insights and recommendations for business decisions.
Faculty & Inclusions
Your Trainer
Faculty
Instructors Details
The programme is designed to be led by instructors with practical knowledge of Python, Jupyter Notebook, Pandas, NumPy, data cleaning, data transformation, data analysis, Matplotlib, Seaborn, exploratory data analysis, dashboards, analytical reports and business decision-making insights.
What's Included
Certificates & Inclusions
- Certificate and inclusions to be confirmed as per programme format.
- Practical learning on Python foundations for data analytics.
- Hands-on data handling using Pandas and NumPy.
- Data cleaning, analysis and visualisation practice.
- Practical business analytics project with insights and recommendations.
Registration
Reserve Your Seat
Need assistance? Our programme advisors are here to help.
Get in TouchNeed Assistance?
Talk to a Programme Advisor
Have questions about your specific industry, or want to enquire about group discounts? Reach out to our programme coordinators directly.
Or register by e-mail
register@princetonacademy.co.inFor Teams
Corporate In-House Training
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Request a Custom In-House ProposalSchedule
Upcoming Python for Data Analytics Sessions
20261117
Online
Questions
Frequently Asked Questions
The programme focuses on Python for data analytics, including Python foundations, Pandas, NumPy, data cleaning, analysis, visualisation and a practical business analytics project.
Yes. Participants learn to set up and work with Jupyter Notebook as part of the Python foundations session.
Yes. The programme covers NumPy arrays, Pandas DataFrames, importing data, selecting, filtering, sorting, transforming and combining datasets.
Yes. Participants learn to handle missing values, duplicates and inconsistent data, and create charts using Matplotlib and Seaborn.
Yes. The programme includes a practical business analytics project involving exploratory data analysis, identifying patterns and trends, developing a dashboard or analytical report, and presenting insights and recommendations.
